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Noise reduction for desert seismic data using spectral kurtosis adaptive bandpass filter

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In view of the heterogeneity and week similarity of random noise in the desert seismic exploration, and lots of random noise focused on low frequency, the traditional bandpass filter and wavelet transform are used to separate the signal and noise. Although there are some denoising effects, the noise cannot be suppressed well, and effective signal is damaged to some extent. Because of the above shortcomings, we propose a bandpass filter denoising method based on spectral kurtosis in this paper. This method is based on the signal and the random noise’s energy distribution characteristics in the frequency domain. First, through short-time Fourier transform (STFT), the spectral kurtosis of noisy signals is obtained. Second, we design a new threshold by the obtained spectral kurtosis, the value of spectral kurtosis greater than the threshold is preserved, and the spectral kurtosis less than the threshold is set to 0. So, the method realises the adaptive choice of the filter passband, getting an adaptive bandpass filter. At the same time, the noise can be suppressed to a greater extent while the effective signal is retained very well. The noise removal results of synthetic data and actual data show that the proposed method has very good denoising performance and amplitude preserving capability.
Czasopismo
Rocznik
Strony
123--131
Opis fizyczny
Bibliogr. 18 poz.
Twórcy
autor
  • College of Communication Engineering, Jilin University, No.5372 Nanhu Road, Changchun 130012, Jilin, China
autor
  • College of Communication Engineering, Jilin University, No.5372 Nanhu Road, Changchun 130012, Jilin, China
autor
  • College of Communication Engineering, Jilin University, No.5372 Nanhu Road, Changchun 130012, Jilin, China
autor
  • College of Communication Engineering, Jilin University, No.5372 Nanhu Road, Changchun 130012, Jilin, China
autor
  • College of Communication Engineering, Jilin University, No.5372 Nanhu Road, Changchun 130012, Jilin, China
autor
  • College of Communication Engineering, Jilin University, No.5372 Nanhu Road, Changchun 130012, Jilin, China
Bibliografia
  • 1. Antoni J (2006) The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech Syst Signal Process 20(2):282–307CrossRefGoogle Scholar
  • 2. Antoni J, Randall RB (2006) The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech Syst Signal Process 20(2):308–331CrossRefGoogle Scholar
  • 3. Chang SG, Yu B, Vetterli M (2002) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process A Publ IEEE Signal Process Soc 9(9):1532–1546CrossRefGoogle Scholar
  • 4. Combet F, Gelman L (2009) Optimal filtering of gear signals for early damage detection based on the spectral kurtosis. Mech Syst Signal Process 23(3):652–668CrossRefGoogle Scholar
  • 5. Cramér H (1971) Structural and statistical problems for a class of stochastic processes. Princeton University Press, PrincetonCrossRefGoogle Scholar
  • 6. Cramér H (2010) Structural and statistical problems for a class of stochastic processes/harald cramér. East Afr J Publ Health 7(2):140–143Google Scholar
  • 7. Dwyer R (1983) Detection of non-Gaussian signals by frequency domain Kurtosis estimation. In: IEEE international conference on acoustics, speech, and signal processing, ICASSP 8:607–610. IEEEGoogle Scholar
  • 8. Johnstone IM, Silverman BW (1997) Wavelet threshold estimators for data with correlated noise. J Roy Stat Soc 59(2):319–351CrossRefGoogle Scholar
  • 9. Kar SS, Maity SP (2016) Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy. Comput Methods Progr Biomed 133:111–132CrossRefGoogle Scholar
  • 10. Liu H, Zhang Z, Liu S, Liu T, Chang Y (2015) Destriping algorithm with L0 sparsity prior for remote sensing images. IEEE International Conference on Image Processing, IEEEGoogle Scholar
  • 11. Lei SF, Ahroon WA, Hamernik RP (1994) The application of frequency and time domain kurtosis to the assessment of hazardous noise exposures. J Acoust Soc Am 96(3):1435–1444CrossRefGoogle Scholar
  • 12. Lin H, Li Y, Yang B, Ma H (2013) Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment. Geophysics 78(6):V229–V237CrossRefGoogle Scholar
  • 13. Sinno Z, Caramanis C, Bovik AC (2018) Towards a closed form second-order natural scene statistics model. IEEE Trans Image Process 27(7):3194–3209CrossRefGoogle Scholar
  • 14. Tian Y, Li Y, Yang B (2014) Variable-eccentricity hyperbolic-trace tfpf for seismic random noise attenuation. IEEE Trans Geosci Remote Sens 52(10):6449–6458CrossRefGoogle Scholar
  • 15. Wu N, Li Y, Tian Y, Zhong T (2016) Trace-transform-based time-frequency filtering for seismic signal enhancement in northeast china. C R Géosci 348(5):360–367CrossRefGoogle Scholar
  • 16. Xiong M, Li Y, Wu N (2014) Random-noise attenuation for seismic data by local parallel radial-trace TFPF. IEEE Trans Geosci Remote Sens 52(7):4025–4031CrossRefGoogle Scholar
  • 17. Zhang C, Li Y, Lin HB, Yang BJ (2015) Seismic random noise attenuation and signal-preserving by multiple directional time-frequency peak filtering. C R Géosci 347(1):2–12CrossRefGoogle Scholar
  • 18. Zhuang G, Li Y, Liu Y, Lin H, Ma H, Wu N (2014) Varying-window-length tfpf in high-resolution radon domain for seismic random noise attenuation. IEEE Geosci Remote Sens Lett 12(2):404–408
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6f59d04c-f509-4d6b-875b-7d7cdd1ade12
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